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公开(公告)号：US20220344055A1
公开(公告)日：2022-10-27
申请号：US17653248
申请日：2022-03-02
发明人： AVIK GHOSE , AVIJIT SAMAL , NASIMUDDIN AHMED , SHIVAM SINGHAL , KARAN BHAVSAR , VIVEK CHANDEL , SUNDEEP KHANDELWAL , HARSH VISHWAKARMA , BHASKAR PAWAR
摘要： Non-communicable diseases (NCDs) are the pandemics of modern era and are generating huge impact in the modern society. Conventional methods are inaccurate due to a challenge in handling data from heterogenous sensors. The present disclosure is capable of tracking fitness parameters of a user even with heterogenous sensors. Initially, the system receives a raw data from a plurality of heterogenous sensors associated with the user. The raw data is further transformed into a metadata format associated with the corresponding sensor. The transformed data is temporally aligned based on a time based slotting. An algorithm pipeline corresponding to a disorder to be analyzed is selected from a Directed Acyclic Graph (DAG) based on a sensor metadata and a plurality of algorithm metadata corresponding to a plurality of algorithms stored in an algorithm database and an algorithm pipeline. The corresponding disorder is analyzed using the algorithm pipeline.
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2.发明公开公开(公告)号：US20230404461A1
公开(公告)日：2023-12-21
申请号：US18329855
申请日：2023-06-06
发明人： VARSHA SHARMA , AYAN MUKHERJEE , MURALI PODUVAL , SUNDEEP KHANDELWAL , ANIRBAN DUTTA CHOUDHURY , CHIRAYATA BHATTACHARYYA
CPC分类号： A61B5/346 , A61B5/7267 , G06T1/00 , G06T2207/20081 , G06T2207/20084
摘要： State of art techniques hardly provide data balancing for multi-label multi-class data. Embodiments of the present disclosure provide a method and system for identifying cardiac abnormality in multi-lead ECGs using a Hybrid Neural Network (HNN) with fulcrum based data re-balancing for data comprising multiclass-multilabel cardiac abnormalities. The fulcrum based dataset re-balancing disclosed enables maintaining natural balance of the data, control the re-sample volume, and still support the lowly represented classes there by aiding proper training of the DL architecture. The HNN disclosed by the method utilizes a hybrid approach of pure CNN, a tuned-down version of ResNet, and a set of handcrafted features from a raw ECG signal that are concatenated prior to predicting the multiclass output for the ECG signal. The number of features is flexible and enables adding additional domain-specific features as needed.
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3.发明公开公开(公告)号：US20240321450A1
公开(公告)日：2024-09-26
申请号：US18393358
申请日：2023-12-21
IPC分类号： G16H50/20 , G06F18/2415
CPC分类号： G16H50/20 , G06F18/2415
摘要： Improvement in the accuracy of disease diagnosis associated with cardiac abnormalities is an open research area. Appropriate feature selection to capture the underlying signs of a disease is critical in Machine Learning (ML) based approaches. A method and system for, determining cardiac abnormalities using chaos-based classification model from multi-lead ECG signals, is disclosed. The method combines the commonly used chaos parameter with other set of chaos-related statistical parameters like non-linearity, self-similarity, Chebyshev distance and spectral flatness for a holistic approach to the study of cardiac abnormalities. The method disclosed thus attempts to use above ML based measures for disease classification. The set of chaos-related features used herein contribute to improving the accuracy of detection of various cardiac diseases arising due to cardiac abnormalities such as Atrial Fibrillation (AF) and the like. The improved accuracy in the detection of AF effectively improves the accuracy in percentage of AF burden.
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公开(公告)号：US20230397822A1
公开(公告)日：2023-12-14
申请号：US18332552
申请日：2023-06-09
CPC分类号： A61B5/02028 , A61B5/366 , A61B5/02405 , A61B5/02108
摘要： This disclosure relates generally to in-silico modeling of hemodynamic patterns of physiologic blood flow. Conventional cardiovascular hemodynamic models depend on neuromodulation schemes (baroreflex autoregulation) and threshold parameters of neuromodulation correlate with physical activities. Thus these models may not work practically for a large set of people due to dependency on prior knowledge of these parameters. The present disclosure enables estimating blood pressure of a subject by estimating cardiac parameters based on the morphology of ECG signal associated with the subject and hence activation delays in cardiac chambers of the in-silico model is reproduced purposefully. In accordance with the present disclosure, the blood pressure of the subject can be estimated using only the ECG signal even if the signal is missed for some time instance(s) or is noisy.
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